Implications of autolysosome- astrocyte-associated signature in the pathogenesis of Alzheimer's disease: evidence from artificial intelligence and multi-omics and clinical validation.
Zhang Congmin C, Song Dandan D
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-beta plaques and neurofibrillary tangles. Dysfunctional cellular clearance mechanisms, particularly autophagy-lysosomal pathways, and reactive astrocytosis are prominent pathological features, yet their interrelationship remains poorly defined. This study aimed to decipher a novel co-expression molecular signature linking autolysosomal dysfunction and astrocyte reactivity in AD pathogenesis. We performed Limma, WGCNA and Xcell algorithms in AD patient hippocampus bulk profiles for enrichment of astrocyte and autolysosome (AA)-associated DEGs. Next, explainable machine learning and consensus clustering enables the identification of AA-associated diagnostic model and molecular subgroups for AD patients at bulk level. Besides, AA-associated central pathogenic factor was identified, and its corresponding biological implications for AD were assessed at AD patient hippocampus single-cell level in temporal and spatial manners. Next deep learning algorithm (Drugreflector) and molecular docking enriched natural compounds for the treatment of AD by targeting AA-associated hub gene. Finally, AD clinical peripheral blood samples were collected for estimation of hub gene expression patterns. 5 AA-associated shared DEGs can elaborate diagnostic and patient stratification capacity for AD patients. HMGCR can be considered as astrocyte-distributed central pathogenic and Berberine-oriented therapeutic target for AD patients. Our findings unveil AA-associated diagnostic model and molecular subgroups coupled with HMGCR center pathogenic and druggable role in AD, which represents an actionable clinical target for AD patients.